Incidence, Impact, and Risk Factors of Adverse Events in Thoracic and Lumbar Spine Fractures: An Ambispective Cohort Analysis of 390 Patients
Bibliographic record
Abstract
Background Context Adverse events (AEs) in thoracic and lumbar spine fractures are common but little is known about the type of AEs that are specific to this population. Further, very little is known about the incidence and clinical impact of these AEs on patients in the presence of traumatic spinal cord injury (TSCI) and whether they are treated operatively or nonoperatively. Purpose To determine the incidence of AEs in patients with thoracic or lumbar spine fractures treated both operatively and nonoperatively and determine their impact on length of stay (LOS). Secondly, determine the difference in incidence of AEs in both neurologically intact and compromised patients. Study Design/Setting Ambispective cohort study at a quaternary referral center. Patient Sample Patients admitted at our institution with thoracic or lumbar fractures from January 2009 to December 2013 were identified. Patients with full spine adverse events severity system data (SAVES) were included. Outcome Measures Number and type of AEs collected from SAVES were assessed. Impact of AE on acute LOS was also determined. Materials and Methods No funding was received or used in this study. Data on intra-, pre-, and postoperative AEs were prospectively collected using the SAVES data collection. Logistic regression was used to model the likelihood of experiencing at least one AE based on patient characteristics. The impact of the total number of AEs experienced by a patient and that of each of the most common AEs on LOS was determined using Poisson regression. Results A total of 390 patients were included in final analysis. Total, 276 patients (70.8%) were treated operatively. Overall 140 patients (36%) experienced neurological deficit as a result of their initial injury. AEs occurred in operatively treated patients 56% of the time and only 13% of the time in the nonoperative group. The presence of neurological deficit increased the risk of AEs especially in high thoracic (T1–T6) trauma increasing the odds of having an adverse event by 12.1 ( p < 0.0001). The most common AEs were urinary tract infections (UTIs) (19.7%), neuropathic pain (12.3%), pneumonias (11.8%), delirium (10.5%), and ileus (6.2%). LOS increased significantly with pneumonia ( p < 0.0001) and delirium ( p = 0.0001). Conclusions The presence of neurological injury and the need for operative fixation of thoracic or lumbar injuries leads to a greater risk of adverse events. Only pneumonia and delirium consistently increase LOS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".